Description
Overview
The most common machine learning library in Python is scikit-learn. This lab walks you through the sklearn
library to perform machine learning tasks.
Step 1 – Tutorial
Review the tutorial at https://scikit-learn.org/stable/tutorial/basic/tutorial.html.
Step 2 – Explore
Download the lab10.py file, which has the following features:
• Imports:
o numpy – for Linear Algebra
o matplotlib.pyplot – for Plotting results
o Scikit-Learn Data Manipulation:
§ datasets
§ sklearn.model_selection.train_test_split
§ sklearn.model_selection.learning_curve
o Scikit-Learn ML Classifiers
§ sklearn.linear_model.LinearRegression
§ sklearn.svm.SVC
§ sklearn.tree.DecisionTreeClassifier
§ sklearn.neighbors.KNeighborsClassifier
• Functions:
o linear_regression(X_train, Y_train, X_test, Y_test)
o support_vector_machine(X_train, Y_train, X_test, Y_test)
o decision_tree(X_train, Y_train, X_test, Y_test)
o k_nearest_neighbors(X_train, Y_train, X_test, Y_test)
o split_test_train(test_percent, X, Y)
o plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None, n_jobs=None,
train_sizes=np.linspace(.1, 1.0, 5))
Step 3 – Complete the Code
A. Setup Classifiers
Each of the four ML functions should have the following basic layout. Use this layout to complete the code for
each.
1. Set an estimator variable equal to the appropriate classifier.
2. Set a model variable equal to the estimator’s fit() method, passing in the X_train and Y_train
parameters. This returns the model created by the classifier based on the training set.
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3. Set a score variable by calling the score() method, passing in the X_test and Y_test parameters. This
returns the average accuracy of the model.
4. Return estimator, model, score
B. Setup Data Sets
1. Load the iris data set from the sklearn.datasets.load_iris() method.
2. Store observed data from iris.data in a variable named X.
3. Store labels for the observed data from iris.target in a variable named y.
Note that X[0] is a vector whose label is y[0].
4. Explore the data. Determine how many classes exist, and how many observations exist within each class.
Is the data balanced?
5. Create a test and training set. Note that split_test_train returns in this order: X_train, X_test, Y_train, Y_test.
Note also that you should decide how large the test set should be. A typical train/test split is 70/30 or
80/20.
C. Perform Machine Learning
1. Call each function to perform the machine learning and create models based on each classifier. Collect
the output in variables, keeping in mind that each function returns estimator, model, and score.
2. Plot the learning curve for each classifier. Call the plot_learning_curve method, passing the appropriate
estimator, a title, and the X and y train variables.
3. Output the value of the testing score results for each model to the console.
D. Analyze Results
Use the information above to analyze the results. Include screenshots of your learning curves and include the
average accuracy scores.
1. Which classifier performed best based on your train/test split? Why do you think it outperformed the
others? Use the screenshots to justify your answer.
2. Try a different train/test split. Did this affect the results? If so, how? Record the screenshots for the new
train/test split.
As a note, the train/test split will be different each time you run the program, which can affect results. Most
often, people run several iterations to determine an overall average accuracy. You only need to run once here.
Submission
Submit your completed lab10.py file and your Lab10.docx file.
Submit to the Lab 10 dropbox at or before Monday, April 12, 2021 by 11:59 PM.
Grading
A letter grade will be assigned for each response. The letter grades are based on both correctness and the
adequacy of answers. Points are assigned as follows:
A B C D F Zero
Excellent Above
Average Average Below
Average Poor No Attempt
10 8 6 4 2 0
Complete the
Code
Step A
Step B
Step C
Step D
Analysis
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